1 citations · 1 across the 4 of their papers we have counts for
11 papers
Wisdom of the Crowd: Reinforcement Learning from Coevolutionary Collective Feedback
Wenzhen Yuan, Shengji Tang, Weihao Lin +8
Reinforcement learning (RL) has significantly enhanced the reasoning capabilities of large language models (LLMs), but its reliance on expensive human-labeled data or complex rewar…
Decouple and Orthogonalize: A Data-Free Framework for LoRA Merging
Shenghe Zheng, Hongzhi Wang, Chenyu Huang +5
With more open-source models available for diverse tasks, model merging has gained attention by combining models into one, reducing training, storage, and inference costs. Current…
Dynamic Base model Shift for Delta Compression
Chenyu Huang, Peng Ye, Shenghe Zheng +4
Transformer-based models with the pretrain-finetune paradigm bring about significant progress, along with the heavy storage and deployment costs of finetuned models on multiple tas…
Breaking the Compression Ceiling: Data-Free Pipeline for Ultra-Efficient Delta Compression
Xiaohui Wang, Peng Ye, Chenyu Huang +5
With the rise of the fine-tuned-pretrained paradigm, storing numerous fine-tuned models for multi-tasking creates significant storage overhead. Delta compression alleviates this by…
FAVOR-Bench: A Comprehensive Benchmark for Fine-Grained Video Motion Understanding
Chongjun Tu, Lin Zhang, Pengtao Chen +5
Multimodal Large Language Models (MLLMs) have shown remarkable capabilities in video content understanding but still struggle with fine-grained motion comprehension. To comprehensi…
TokenCarve: Information-Preserving Visual Token Compression in Multimodal Large Language Models
Xudong Tan, Peng Ye, Chongjun Tu +5
Multimodal Large Language Models (MLLMs) are becoming increasingly popular, while the high computational cost associated with multimodal data input, particularly from visual tokens…